Artificial intelligence: How to avoid racist algorithms

Image caption
Only white babies appear in a search for “babies” on Microsoft search engine Bing…

There is growing concern that many of the algorithms that make decisions about our lives – from what we see on the internet to how likely we are to become victims or instigators of crime – are trained on data sets that do not include a diverse range of people.

The result can be that the decision-making becomes inherently biased, albeit accidentally.

Try searching online for an image of “hands” or “babies” using any of the big search engines and you are likely to find largely white results.

In 2015, graphic designer Johanna Burai created the World White Web project after searching for an image of human hands and finding exclusively white hands in the top image results on Google.

Her website offers “alternative” hand pictures that can be used by content creators online to redress the balance and thus be picked up by the search engine.

Google says its image search results are “a reflection of content from across the web, including the frequency with which types of images appear and the way they’re described online” and are not connected to its “values”.

Ms Burai, who no longer maintains her website, believes things have improved.

“I think it’s getting better… people see the problem,” she said.

“When I started the project people were shocked. Now there’s much more awareness.”

“If you have not that many people of colour within the data set, then you might actually have biased results,” he said at the time.

On a more serious note, AI software used in the US to predict which convicted criminals might reoffend, was found to be more likely to incorrectly identify black offenders as high risk and white offenders as low risk, according to a study by the website Propublica (the software firm disputed these findings).

Suresh Venkatasubramanian, an associate professor at the University of Utah school of computing, says creators of AI need to act now while the problem is still visible.

“The worst that can happen is that things will change and we won’t realise it,” he told the BBC.

“In other words the concern has been that the bias, or skew, in decision-making will shift from things we recognise as human prejudice to things we no longer recognise and therefore cannot detect – because we will take the decision-making for granted.”

Image copyrightGetty Images

Image caption
Are we accidentally programming prejudiced robots?

He is however optimistic about tech’s progress.

“To say all algorithms have racist manifestations doesn’t make sense to me,” he says.

“Not because it’s impossible but because that’s not how it’s actually working.

“In the last three to four years what’s picked up is the discussion around the problems and possible solutions,” he adds.

He offers a number of these:

creating better and more diverse data sets with which to train the algorithms (they learn by processing thousands of, for example, images)

sharing best practice among software vendors, and

building algorithms which explain their decision making so that any bias can be understood.

Ms Buolamwini says she is hopeful that the situation will improve if people are more aware of the potential problems.

“Any technology that we create is going to reflect both our aspirations and our limitations,” she says.

“If we are limited when it comes to being inclusive that’s going to be reflected in the robots we develop or the tech that’s incorporated within the robots.”

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